Static and Dynamic Reservoir Rock Typing of Highly Heterogeneous Fluvial Delta Marine Sands Deposited in Mesopotamian Basin

Abstract Geological reservoir study is a crucial step to understand the reservoir properties that drive hydrocarbon flow behavior as part of reservoir life cycle. Among them, sandstone reservoirs have a lot of challenges when the data is scarce to integrate and link between the different disciplines. Nevertheless, a collaboration between geologists and petrophysicists paved a way to systematically fill data gaps which resulted in understanding of petrophysical distributions and the degree of heterogeneity. The methodology starts with a principal component analysis of logs to identify useful data for prediction. It uses the core description, XRD and the thin section to reveal the major diagenetic events and critical mineralogy. Simultaneously, this workflow calculates the hydraulic flow units (HFU) based on core data (K, Phi) flow zone indicators, discrete rock types and global hydraulic elements. The stratigraphically modified Lorenz plot is used to identify storage capacity and flow capacity to detect high permeability flags. The static characteristics are linked with the dynamic HFUs to decipher cloud boundaries that have unique geological and fluid-flow parameters. The core description shows the following depo-zones within a fluvial delta marine sand: delta front mouth bar, distributary channels, distributary mouth bar and prodelta rock types. The thin section indicates the influence of kaolinite and cementation in some reservoir intervals. This knowledge was incorporated to calculate permeability with a higher precision (R2= 0.9 – 0.98) in comparison with core permeability. The FZI method showed seven rock types. Stratigraphically modified Lorenz plot used pore-throat radius, storage capacity, flow capacity, and reservoir speed characteristics. This technique resulted in 5 to 8 hydraulic flow units that divided the Poro-Perm cloud into barrier, baffle, normal flow, and super flow based on slope changes. The distributary channel has the best rock type, while the delta front mouth bar and distributary mouth bar have the medium to the worst rock types while the prodelta has the poorest rock types. Through the integration of sedimentological and petrophysical data, this workflow enables the characterization of highly heterogeneous sandstone reservoirs. Its result improves the prediction of rock type, permeability, and water saturation properties in geological models. Furthermore, it optimizes the number of RRTs to reduce uncertainty resulting in the creation of a robust static model and a better prediction during the implementation of the dynamic model.